* bibkeys.txt: Rename WuAzam2001metrics to WuAza2001metrics.

Author: MLopez-Ibanez

bibkeys.txt

@@ -32,7 +32,7 @@ Muller1959sphere
 RubMel1998simulation
 SchEsqLarCoe2012tec
 VelLam1998gp
-WuAzam2001metrics
+WuAza2001metrics
 ZhoZhaJin2009igdx
 ZitThi1998ppsn
 ZitThiLauFon2003:tec

python/doc/source/REFERENCES.bib

@@ -361,7 +361,7 @@ @article{SchEsqLarCoe2012tec
   doi = {10.1109/TEVC.2011.2161872}
 }
 
-@article{WuAzam2001metrics,
+@article{WuAza2001metrics,
   author = {J. Wu and S. Azam},
   title = {Metrics for Quality Assessment of a Multiobjective Design
                   Optimization Solution Set},

python/src/moocore/_moocore.py

@@ -461,7 +461,7 @@ def hypervolume(
     for speed.
 
     For 5D or higher and up to 15 points, the implementation uses the
-    inclusion-exclusion algorithm :footcite:p:`WuAzam2001metrics`, which has
+    inclusion-exclusion algorithm :footcite:p:`WuAza2001metrics`, which has
     :math:`O(m 2^{n})` time and :math:`O(n\cdot m)` space complexity, where
     :math:`m` is the dimension of the points, but it is very fast for such
     small sets.  For larger number of points, it uses a recursive algorithm

r/R/hv.R

@@ -58,7 +58,7 @@
 #' speed.
 #'
 #' For 5D or higher and up to 15 points, the implementation uses the
-#' inclusion-exclusion algorithm \citep{WuAzam2001metrics}, which has \eqn{O(m
+#' inclusion-exclusion algorithm \citep{WuAza2001metrics}, which has \eqn{O(m
 #' 2^{n})} time and \eqn{O(n\cdot m)} space complexity, but it is very fast for
 #' such small sets.  For larger number of points, it uses a recursive algorithm
 #' \citep{FonPaqLop06:hypervolume} with \eqn{\text{HV4D}^{+}} as the base case,

r/inst/REFERENCES.bib

@@ -361,7 +361,7 @@ @article{SchEsqLarCoe2012tec
   doi = {10.1109/TEVC.2011.2161872}
 }
 
-@article{WuAzam2001metrics,
+@article{WuAza2001metrics,
   author = {J. Wu and S. Azam},
   title = {Metrics for Quality Assessment of a Multiobjective Design
                   Optimization Solution Set},